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Cost Effectiveness and Economic Impact of the KineSpring® Knee Implant System in the Treatment of Knee Osteoarthritis in Spain

2015· article· en· W4229488586 on OpenAlexaff
Dan Strain, Chuan Silvia Li, Mark Phillips, Olga Monteagudo-Piqueras, Mohit Bhandari

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2015
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsHamilton General HospitalMcMaster University
Fundersnot available
KeywordsUnicompartmental knee arthroplastyMedicineHigh tibial osteotomyQuality-adjusted life yearOsteoarthritisCost effectivenessImplantArthroplastySurgery

Abstract

fetched live from OpenAlex

We investigated the efficacy and cost effectiveness of the KineSpring System in the Spanish healthcare system, as compared to other standard treatments methods. Cost-utility ratios were calculated using derived cost data and we calculated quality-adjusted life years (QALYs) gained for each method of treatment. Cost-utility ratios were calculated assuming lifetime and 10-year durability. Assuming lifetime durability, cost-utility ratios of total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), high tibial osteotomy (HTO), KineSpring System, and conservative treatments, compared to no treatment, are €2348 ± 70/QALYs, €2040 ± 61/QALYs, €2281 ± 68/ QALY, €1669 ± 268/QALYs, and €11,688 ± 2185/QALYs, respectively. Assuming a treatment durability of 10 years, the cost-utility ratio of TKA, UKA, HTO, KineSpring System, and conservative treatments, compared to no treatment, are €4884 ± 323/QALYs, €4243 ± 280/QALYs, €4744 ± 313/QALYs, €3757 ± 1353/QALYs, and €10,575 ± 4414/QALYs, respectively. In comparison to current standard-of-care treatments, the KineSpring System has a favorable cost-utility ratio, making it an effective treatment option and a suitable cost-saving alternative. The KineSpring System is associated with lower cost and increased QALYs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.322
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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